title: DSIM: a DisSIMilarity-based image clutter metric for targeting performance.
abstract: Previous image clutter metrics were proposed on the thought that clutter was just a perceptual effect, while we identify clutter as both perceptual and cognitive effects. Under this identification, we give a new definition of image clutter metric by analyzing the research results in the fields of visual psychology and psychophysics. According to the definition, we further put forward a DisSIMilarity (DSIM) based image clutter metric, which can also be taken as a kind of HVS-based signal-to-clutter ratio. The earlier image clutter metrics produced limited success in predicting targeting performance mainly since they did not consider brain cognitive characteristics. We develop a brain cognitive dissimilarity measure (BCDM) as a quantitative estimate of the selection weights which are allocated by brain attentional mechanism to affect visual selection processes. A human vision perceptual dissimilarity measure (VPDM), fully embodying vision perceptual properties, is first established between the target and clutter images, and then we utilize the BCDM between the two images as selection weights to pool the VPDM to be a clutter metric, which can be called DSIM metric. The metric is tested in Search_2 dataset provided by TNO Human Factors Research Institute of Netherlands. Error analysis and correlation tests demonstrate that the DSIM metric makes a more significant improvement than previously proposed metrics in predicting 62 observers' targeting performances including detection probability, false alarm probability and search time.
authors.affiliation: Graduate University of Chinese Academy of Sciences, Beijing, China. djxu@sia.cn
authors.forename: Dejiang
authors.initials: D
authors.lastname: Xu
authors.forename: Zelin
authors.initials: Z
authors.lastname: Shi
mesh_terms: D000465:Algorithms; D001288:Attention / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008960:Models, Psychological; D010364:Pattern Recognition, Visual / Q000502:physiology; D013647:Task Performance and Analysis; D014796:Visual Perception / Q000502:physiology*